The application of mixture-of-experts (MoE) is gaining popularity due to its ability to improve model's performance. In an MoE structure, the gate layer plays a significant role in distinguishing and routing input features to different experts. This enables each expert to specialize in processing their corresponding sub-tasks. However, the gate's routing mechanism also gives rise to "narrow vision": the individual MoE's expert fails to use more samples in learning the allocated subtask, which in turn limits the MoE to further improve its generalization ability. To effectively address this, we propose a method called Mixture-of-Distilled-Expert (MoDE), which applies moderate mutual distillation among experts to enable each expert to pick up more features learned by other experts and gain more accurate perceptions on their allocated sub-tasks. We conduct plenty experiments including tabular, NLP and CV datasets, which shows MoDE's effectiveness, universality and robustness. Furthermore, we develop a parallel study through innovatively constructing "expert probing", to experimentally prove why MoDE works: moderate distilling knowledge from other experts can improve each individual expert's test performances on their assigned tasks, leading to MoE's overall performance improvement.

Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer personalized results for each request, resulting in a massive energy consumption and carbon emission that raises concern. This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. For each stage (e.g., recall, pre-ranking, ranking, etc.) of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage. We refer to the combinations of actions in all stages as action chains. A reward score is estimated for each action chain, followed by dynamic primal-dual optimization considering both the reward and computation budget. Extensive experiments verify the effectiveness of the framework, reducing computation consumption by 41% in an industrial mobile application while maintaining commercial revenue. Moreover, the proposed framework saves approximately 5000kWh of electricity and reduces 3 tons of carbon emissions per day.

Due to the progressive advancement of trustworthy machine learning algorithms, fairness in recommender systems is attracting increasing attention and is often considered from the perspective of users. Conventional fairness-aware recommendation models assume that user preferences remain the same between the training set and the testing set. However, this assumption is arguable in reality, where user preference can shift in the testing set due to the natural spatial or temporal heterogeneity. It is concerning that conventional fairness-aware models may be unaware of such distribution shifts, leading to a sharp decline in the model performance. To address the distribution shift problem, we propose a robust fairness-aware recommendation framework based on Distributionally Robust Optimization (DRO) technique. In specific, we assign learnable weights for each sample to approximate the distributions that leads to the worst-case model performance, and then optimize the fairness-aware recommendation model to improve the worst-case performance in terms of both fairness and recommendation accuracy. By iteratively updating the weights and the model parameter, our framework can be robust to unseen testing sets. To ease the learning difficulty of DRO, we use a hard clustering technique to reduce the number of learnable sample weights. To optimize our framework in a full differentiable manner, we soften the above clustering strategy. Empirically, we conduct extensive experiments based on four real-world datasets to verify the effectiveness of our proposed framework.

Uplift modeling aims to model the incremental impact of a treatment on an individual outcome, which has attracted great interests of researchers and practitioners from different communities. Existing uplift modeling methods rely on either the data collected from randomized controlled trials (RCTs) or the observational data which is more realistic. However, we notice that on the observational data, it is often the case that only a small number of subjects receive treatment, but finally infer the uplift on a much large group of subjects. Such highly imbalanced data is common in various fields such as marketing and medical treatment but it is rarely handled by existing works. In this paper, we theoretically and quantitatively prove that the existing representative methods, transformed outcome (TOM) and doubly robust (DR), suffer from large bias and deviation on highly imbalanced datasets with skewed propensity scores, mainly because they are proportional to the reciprocal of the propensity score. To reduce the bias and deviation of uplift modeling with an imbalanced dataset, we propose an imbalance-aware uplift modeling (IAUM) method via constructing a robust proxy outcome, which adaptively combines the doubly robust estimator and the imputed treatment effects based on the propensity score. We theoretically prove that IAUM can obtain a better bias-variance trade-off than existing methods on a highly imbalanced dataset. We conduct extensive experiments on a synthetic dataset and two real-world datasets, and the experimental results well demonstrate the superiority of our method over state-of-the-art.

Modeling sequential data is essential to many applications such as natural language processing, recommendation systems, time series predictions, anomaly detection, etc. When processing sequential data, one of the critical issues is how to capture the temporal-correlation among events. Though prevalent and effective in many applications, conventional approaches such as RNNs and Transformers, struggle with handling the non-stationary characteristics (i.e., such temporal-correlation among events would change over time), which is indeed encountered in many real-world scenarios. In this paper, we present a non-stationary time-aware kernelized attention approach for input sequences of neural networks. By constructing the Generalized Spectral Mixture Kernel (GSMK), and integrating it to the attention mechanism, we mathematically reveal its representation capability in terms of the time-dependent temporal-correlation. Following that, a novel neural network structure is proposed, which would enable us to encode both stationary and non-stationary time event series. Finally, we demonstrate the performance of the proposed method on both synthetic data which presents the theoretical insights, and a variety of real-world datasets which shows its competitive performance against related work.

Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign.

Encoding a large-scale network into a low-dimensional space is a fundamental step for various network analytic problems, such as node classification, link prediction, community detection, etc. Existing methods focus on learning the network representation from either the static graphs or time-aggregated graphs (e.g., time-evolving graphs). However, many real systems are not static or time-aggregated as the nodes and edges are timestamped and dynamically changing over time. For examples, in anti-money laundering analysis, cycles formed with time-ordered transactions might be red flags in online transaction networks; in novelty detection, a star-shaped structure appearing in a short burst might be an underlying hot topic in social networks. Existing embedding models might not be able to well preserve such fine-grained network dynamics due to the incapability of dealing with continuous-time and the negligence of fine-grained interactions. To bridge this gap, in this paper, we propose a fine-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. In particular, we start from the notion of fine-grained temporal networks, where the temporal network can be represented as a series of timestamped nodes and edges. Then, we propose the time-reinforced random walk (TRRW) with a bi-level context sampling strategy to explore the essential structures and temporal contexts in temporal networks. Extensive experimental results on real graphs demonstrate the efficacy of our FiGTNE framework.

With the revolution of mobile internet, online finance has grown explosively. In this new area, one challenge of significant importance is how to effectively deliver the financial products or services to a set of target users by marketing. Given a product or service to be promoted and a set of users as seeds, audience expansion is such a targeting technique, which aims to find potential audience among a large number of users. However, in the context of finance, financial products and services are dynamic in nature as they co-vary with the socio-economic environment. Moreover, marketing campaigns for promoting products or services always consist of different rules of play, even for the same type of products or services. As a result, there is a strong demand for the timeliness of seeds in financial targeting. Conventional one-stage audience expansion methods, which generate expanded users by expanding over seeds, would encounter two problems under this setting: (1) the seeds would inevitably involve a number of users that are not representative for expansion, and direct expansion over these noisy seeds would dramatically deteriorate the performance; (2) one-stage expansion over fixed seeds cannot timely and accurately capture users' preferences over the currently running campaign due to the lack of timeliness of seeds.

Delineating the seismic fault, which is an important type of geologic structures in seismic images, is a key step for seismic interpretation. Comparing with conventional methods that design a number of hand-crafted features based on the observed characteristics of the seismic fault, convolutional neural networks (CNNs) have proven to be more powerful for automatically learning effective representations. However, the CNN usually serves as a black box in the process of training and inference, which would lead to trust issues. The inability of humans to understand the CNN would be more problematic, especially in critical areas like seismic exploration, medicine and financial markets. To include domain knowledge to improve the interpretability of the CNN, we propose to jointly optimize the prediction accuracy and consistency between explanations of the neural network and domain knowledge. Taking the seismic fault segmentation as an example, we show that the proposed method not only gives reasonable explanations for its predictions, but also more accurately predicts faults than the baseline model.

Recently, in order to take a preemptive opportunity in the mobile economy, the Internet companies conduct thousands of marketing campaigns every day, to promote their mobile products and services. In the mobile marketing scenario, one of the fundamental issues is the audience expansion task for marketing campaigns. Given a set of seed users, audience expansion aims to seek more users (audiences), who are similar to the seeds and will finish the business goal of the targeted campaign (ie convert). However, the problem is challenging in three aspects. First, a company will run hundreds of campaigns to serve massive users every day. The requirements of scalability and timeliness make training model for each campaign extremely resource-consuming thus impractical. Therefore, we proposed to solve the problem in a two-stage manner, in which the offline stage employs heavyweight user representation learning and the online stage performs embedding-based lightweight audience expansion. Second, conventional two-stage audience expansion systems neglect the high-order user-campaign interactions and usually generate entangled user embeddings, thus fail to achieve high-quality user representation. Third, the seeds, which are usually provided by experts or collected from users' feedbacks, could be noisy and cannot cover the entire actual audiences, thus introduce coverage bias. Unfortunately, to our best knowledge, none of the related literatures tackle this crucial issue of audience expansion.

Many complex systems with relational data can be naturally represented as dynamic processes on graphs, with the addition/deletion of nodes and edges over time. For such graphs, network embedding provides an important class of tools for leveraging the node proximity to learn a low-dimensional representation before using the off-the-shelf machine learning models. However, for dynamic graphs, most, if not all, embedding approaches rely on various hyper-parameters to extract spatial and temporal context information, which differ from task to task and from data to data. Besides, many regulated industries (e.g., finance, health care) require the learning models to be interpretable and the output results to meet compliance. Therefore, a natural research question is how we can jointly model the spatial and temporal context information and learn a unique network representation, while being able to provide interpretable inference over the observed data. To address this question, we propose a generic graph attention neural mechanism named STANE, which guides the context sampling process to focus on the crucial part of the data. Moreover, to interpret the network embedding results, STANE enables the end users to investigate the graph context distributions along three dimensions (i.e., nodes, training window length, and time). We perform extensive experiments regarding quantitative evaluation and case studies, which demonstrate the effectiveness and interpretability of STANE.